Sustained operation of solar photovoltaic assets hinges on accurate detection and prioritization of surface faults across vast, geographically distributed modules. While multi modal imaging strategies are popular, they introduce logistical and economic barriers for routine farm level deployment. This work demonstrates that deep learning and classical machine learning may be judiciously combined to achieve robust surface anomaly categorization and severity estimation from planar visible band imagery alone. We introduce TinyViT which is a compact pipeline integrating Transformer based segmentation, spectral-spatial feature engineering, and ensemble regression. The system ingests consumer grade color camera mosaics of PV panels, classifies seven nuanced surface faults, and generates actionable severity grades for maintenance triage. By eliminating reliance on electroluminescence or IR sensors, our method enables affordable, scalable upkeep for resource limited installations, and advances the state of solar health monitoring toward universal field accessibility. Experiments on real public world datasets validate both classification and regression sub modules, achieving accuracy and interpretability competitive with specialized approaches.
@article{arxiv.2512.00117,
title = {TinyViT: Field Deployable Transformer Pipeline for Solar Panel Surface Fault and Severity Screening},
author = {Ishwaryah Pandiarajan and Mohamed Mansoor Roomi Sindha and Uma Maheswari Pandyan and Sharafia N},
journal= {arXiv preprint arXiv:2512.00117},
year = {2025}
}